University of Oulu

D. Xu et al., "Edge Intelligence: Empowering Intelligence to the Edge of Network," in Proceedings of the IEEE, vol. 109, no. 11, pp. 1778-1837, Nov. 2021, doi: 10.1109/JPROC.2021.3119950

Edge intelligence : empowering intelligence to the edge of network

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Author: Xu, Dianlei1,2; Li, Tong1,3; Li, Yong2;
Organizations: 1Department of Computer Science, University of Helsinki, 00014 Helsinki, Finland
2Beijing National Research Center for Information Science and Technology (BNRist), Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
3Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong
4Department of Computer Science, Norwegian University of Science and Technology, Norway
5Center of Ubiquitous Computing, University of Oulu, Finland
6School of Electronics Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China
7Computer Laboratory, University of Cambridge, William Gates Building, 15 JJ Thomson Avenue, Cambridge CB3 0FD, UK
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 7.6 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2022031824136
Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2022-03-18
Description:

Abstract

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.

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Series: Proceedings of the IEEE
ISSN: 0018-9219
ISSN-E: 1558-2256
ISSN-L: 0018-9219
Volume: 109
Issue: 11
Pages: 1778 - 1837
DOI: 10.1109/JPROC.2021.3119950
OADOI: https://oadoi.org/10.1109/JPROC.2021.3119950
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Subjects:
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